摘要
基于SVD的人脸识别算法具有共同的缺点,即不同人脸图像对应的奇异值向量所在的基空间不一致,从而造成识别率低下。该文分析2种改进的类估计基空间奇异值分解算法(CSVD),通过对比实验选择出其中一种具有优势的CSVD算法。并在特征提取环节,提出CSVD算法与非负矩阵因子算法特征数据相融合的人脸识别算法。在ORL数据库上的实验结果表明,该结合方法有效地提高了识别率和训练速度。
The face recognition algorithms based on SVD have low recognition accuracy due to the common essential defect which singular value vector of arbitrary two face images have the different basis spaces in general. According to this, two improved class estimated basis space singular value de-composition methods are analyzed. A superior CSVD method is selected after comparing with each other. And in the feature extraction process, a new face recognition method based on CSVD and non Negative Matrix Factorization（NMF） is presented. By combining both methods, for ORL database, the better recognition performance is obtained.
出处
《计算机工程》
CAS
CSCD
北大核心
2009年第3期214-216,共3页
Computer Engineering
关键词
类估计基空间奇异值分解
非负矩阵因子
特征提取
Class estimated basis Space singular Value De-composition（CSVD）
non Negative Matrix Factorization（NMF）
feature extraction
